Pain is multifactorial in origin with both genetic and environmental factors contributing to individual variations. Candidate gene studies on the basis of biological hypotheses have been performed to identify relevant genetic variation in complex traits such as pain. However, the complicated mesh of contributing factors and the thousands of molecules involved in different pain phenotypes makes it difficult to detect responsible genetic variations for an individuals unique susceptibility to pain. It is unlikely that common variations in a single gene act dominantly on pain;rather, the contribution of each gene seems to be subtle, acting on one of multiple pain pathways, making its signal difficult to detect. Even though pain has been one of the most significant and frequent problems affecting quality of life for thousands of years, analgesic therapy is still largely limited to opioids and aspirin-like drugs, with the limitations of these drug classes. The combined impact of the rapid increase in knowledge of diseases and the ability to apply powerful and high capacity technology has raised expectations for more effective and safer medicines for pain management. Developing new treatment strategies for the pain management is also critically dependent on identifying new target molecules and defining pain phenotypes for specific types of pain. Therefore the first step of this project has been to define the characteristics of experimental and clinical pain phenotypes. Contributing factors such as gender, ethnicity and psychological factors predominate over the role of genetic factors in pain and analgesic responses when evaluating groups of patients (Kim et al. 2004a;Kim et al. 2004b). But genetic variability may be important at the level of the individual (Dionne et al. 2005). Based on this project, we found haploblocks from candidate pain genes for each major ethnic population. Human haplotype data of pain related genes provide basic information for the genetic association studies of pain sensitivity and responses to analgesics. We also applied this haplotype data to find the association with experimental pain sensitivity and verified that this method is practically useful in investigating the role of genetics in pain sensitivity (Kim et al. 2006, JMG). We also have performed individual SNP association studies of candidate genes and reported controversial result to the previous studies published in Science, which may be biased with population stratification and small sample size (Kim et al. 2006, Molecular Pain). Efforts of investigating the interactions between psychological factors and pain sensitivity along with their genetic profiles were continued and some of the preliminary reports were presented at the American Pain Society and two of our presentations won Young Investigator Awards (Mittal et al., Lee et al.). Meanwhile we investigated the influence of the genetic variations in prostaglandin synthesis on the clinically induced pain and analgesic responses with oral surgery model. From this, we found that homozygous G/G patients of SNP in the promoter region (-765) of COX-2 gene showed significantly different responses to common analgesic drugs compared to G/C heterozygous and C/C homozygous patients (Lee et al. 2006, CPT). Based on these works, we were invited for review articles from International Association for the Study of Pain (Kim et al. 2005) and Pharmacological Review (in preparation) as well as for symposiums of the annual meeting of American Pain Society in year 2007. We recently initiated whole genome scan study related to clinical pain sensitivity using 500,000 SNPs assay. We have analyzed 60 samples so far and the analysis revealed that 7 SNPs show associations with maximum post-operative pain after local anesthetics offset at the level of p <10-6. Four of them are uncharacterized, two of them are located in untranslated region (LOC283867 and SIPA1L1)and one is located in an intron (CDKAL1). Further characterization of these 7 regions with dense genotyping may identify genetic loci that contribute to interindividual variability in pain due to tissue injury and the acute inflammation responses following minor surgery. Based on whole genome scale investigation, we plan to narrow the candidate regions from the human genome to scan more densely. Detailed information of the candidate regions with SNPs may provide knowledge for the genetic role in pain and analgesic responses on an individual patient basis. Finally, we can suggest genomic tools such as a human pain gene chip to develop new drugs and test them based on individual genetic information. The role of genetic factors on clinical pain will continue to be studied in acute pain using genotyping, gene and protein expression, and patient reported outcomes to better understand the reciprocal interplay between these factors and the inflammatory cascade. These data will be analyzed with whole genome scan, microarray, ELISA, SNP genotyping and real time PCR. For chronic pain, patients with well characterized neuropathic pain and fibromyalgia will be recruited and similar techniques used to analyze molecular-genetic mechanisms of chronic pain. From these results along with biological knowledge of pain pathways, we will be able to suggest a human pain gene chip to characterize molecular-genetic mechanisms of pain and analgesia at the level of the individual, suggest new targets for analgesic drugs and test the efficacy and adverse reactions of newly developed or currently used drugs.